Key Responsibilities:

- Model Deployment & Productionalization: Wrap data science prototypes into production-ready software, deploying models as scalable APIs, microservices, or embedded systems.

- CI/CD for Machine Learning (CT): Build and maintain automated pipelines for continuous integration, continuous deployment, and continuous training (CT) of ML models.

- Data & Feature Engineering at Scale: Collaborate with data teams to design and optimize high-throughput data pipelines and implement scalable feature stores to support both real-time and batch scoring.

- Infrastructure & Scalability: Configure and manage cloud infrastructure, orchestration tools, and containerized environments to ensure reliable model scaling under heavy user loads.

- Monitoring & Drift Detection: Set up comprehensive logging and alerting frameworks to monitor model inference latency, system health, data drift, and model performance degradation over time.

- Collaboration & Optimization: Partner with Data Scientists to optimize algorithms for production efficiency (e.g., model quantization, memory optimization) and work with traditional Software Engineers to integrate ML outputs cleanly into client-facing products.

Required Qualifications & Education:

Key Skills:

Core Engineering Skills:

- Production Programming: High proficiency in Python, Java, C++, or Go, with an emphasis on writing clean, modular, and testable object-oriented code.

- Containerization & Orchestration: Strong experience with Docker and Kubernetes for containerizing and orchestrating distributed ML workloads.

- ML Frameworks: Practical experience with production-focused frameworks such as PyTorch, TensorFlow, Scikit-Learn, or XGBoost.

- Software Best Practices: Solid understanding of Git, unit testing, system architecture, and traditional CI/CD pipelines (e.g., GitHub Actions, Jenkins).

Preferred Skills (Highly Desirable):

- Familiarity with dedicated MLOps platforms (e.g., MLflow, Kubeflow, SageMaker, or Vertex AI).

- Experience with big data distributed processing frameworks (like Apache Spark, PySpark, or Ray).

- Experience working with SQL/NoSQL databases and data warehouse systems (e.g., Snowflake, BigQuery).

- Knowledge of building and managing feature stores (e.g., Feast).